T-test based Alzheimer’s disease diagnosis with multi-feature in MRIs

Diagnosing Alzheimer’s disease (AD) with magnetic resonance imaging (MRI) has attracted increasing attention. In this paper, we propose a new feature selection method for AD diagnosis by selecting interested structures in brain MRI. In the proposed method, P-Value is used to obtain the independent principal features, and the structures that have large values are selected as interested structures. P-Value for every voxel is calculated by T-test between different image classes, then the average P-Value for every brain tissue is obtained. After these operations, we firstly use Statistical Parametric Mapping (SPM) software to pre-process MRI, secondly select interested structures based on T-test, then extract different texture characteristics as multi-feature, finally classify the images to diagnose AD by collaborative representation based classification (CRC). Extensive experiments were conducted to evaluate the proposed method, and the comparison results indicate that it achieves better performance in contrast with several existing algorithms.

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